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Molecule Identification with Rotational Spectroscopy and Probabilistic Deep Learning
[article]
2020
arXiv
pre-print
A proof-of-concept framework for identifying molecules of unknown elemental composition and structure using experimental rotational data and probabilistic deep learning is presented. Using a minimal set of input data determined experimentally, we describe four neural network architectures that yield information to assist in the identification of an unknown molecule. The first architecture translates spectroscopic parameters into Coulomb matrix eigenspectra, as a method of recovering chemical
arXiv:2003.12388v2
fatcat:t7n2ichaondllh3lreebccj5zi